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Journals / Iğdır Üniversitesi Sosyal Bilimler Dergisi / 2026 / Sayı: 36

The Effect of Sectoral Diversification on Return and Risk in Portfolio Management: An Application in Borsa Istanbul

Portföy Yönetiminde Sektörel Çeşitlendirmenin Getiri ve Risk Üzerindeki Etkisi: Borsa İstanbul'da Bir Uygulama

Ahmet Akusta(KONYA TEKNİK ÜNİVERSİTESİ)0000-0002-5160-3210
Pages
162–183
Publication Date
2024-05-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The insights gleaned offer valuable guidance for investors, emphasizing the delicate balance between risk mitigation and return optimization in a diversified portfolio.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper investigates sectoral diversification within Borsa Istanbul, aiming to elucidate its impact on portfolio risk and return dynamics.
📊
Key Findings & Data:Spanning the timeframe from 2020 to 2022, the study meticulously analyzes stocks from pivotal sectors, including banking, energy, and iron and steel.
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Conclusion & Impact:The insights gleaned offer valuable guidance for investors, emphasizing the delicate balance between risk mitigation and return optimization in a diversified portfolio.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Portföy yönetimi · Getiri ve risk · Monte Carlo simülasyonları

Abstract

This paper investigates sectoral diversification within Borsa Istanbul, aiming to elucidate its impact on portfolio risk and return dynamics. Spanning the timeframe from 2020 to 2022, the study meticulously analyzes stocks from pivotal sectors, including banking, energy, and iron and steel. Employing a robust methodology, the research harnesses Monte Carlo simulations to generate many hypothetical portfolios, subsequently evaluating them on the Efficient Frontier to identify optimal risk-return trade-offs. Key performance metrics, such as the Sharpe Ratio, Sortino Ratio, and Maximum Drawdown, further enrich the analysis, providing a granular view of portfolio behaviors. The significance of this study lies in its bridging of theoretical constructs of diversification with the tangible realities of an emerging market like Borsa Istanbul. Our main findings underscore the potential benefits of sectoral diversification while highlighting the complexities inherent in portfolio construction. The insights gleaned offer valuable guidance for investors, emphasizing the delicate balance between risk mitigation and return optimization in a diversified portfolio.

Özet

Bu çalışma, Borsa İstanbul içindeki sektörel çeşitlendirmeyi inceleyerek, portföy riski ve getiri dinamikleri üzerindeki etkisini aydınlatmayı amaçlamaktadır. 2020'den 2022'ye kadar olan dönemi kapsayan bu çalışma, bankacılık, enerji ve demir-çelik gibi önemli sektörlerden gelen hisse senetlerini ayrıntılı bir şekilde analiz etmektedir. Sağlam bir metodoloji kullanarak, araştırma, Monte Carlo simülasyonlarını kullanarak birçok hipotetik portföy oluşturur ve ardından bunları Verimli Sınır üzerinde değerlendirerek optimal risk-getiri dengelemelerini belirlemeye çalışır. Sharpe Oranı, Sortino Oranı ve Maksimum Çekilme gibi temel performans ölçütleri, analizi daha da zenginleştirerek portföy davranışlarının ayrıntılı bir görünümünü sunar. Bu çalışmanın önemi, çeşitlendirmenin teorik yapılarını Borsa İstanbul gibi yeni bir pazarın somut gerçekleriyle birleştirmesinde yatmaktadır. Ana bulgularımız, sektörel çeşitlendirmenin potansiyel faydalarını vurgularken, portföy oluşturmanın içerdiği karmaşıklıkları da ortaya koymaktadır. Elde edilen görüşler, yatırımcılar için değerli rehberlik sunar ve çeşitlendirilmiş bir portföyde risk azaltma ve getiri optimize etme arasındaki ince dengeyi vurgular. Ayrıca, bu çalışmanın, yatırım stratejileri geliştiren profesyoneller için önemli bir kaynak olabileceğini belirtmek önemlidir.

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Cite This Article

Akusta, A. (2024). The Effect of Sectoral Diversification on Return and Risk in Portfolio Management: An Application in Borsa Istanbul. Iğdır Üniversitesi Sosyal Bilimler Dergisi, Sayı: 36, 162–183. https://doi.org/10.54600/igdirsosbilder.1385110

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References (40)

  1. Aliu, F., Aliu, F., Nuhiu, A., & Preniqi, N. (2021). Diversification Perspectives of a Single Equity Market: Analysis on the Example of Selected CEE Countries. Comparative Economic Research Central and Eastern Europe. https://doi.org/10.18778/1508-2008.24.32
  2. Allan, P. D. (2001). A Portfolio Management Approach to Assessing Acquisition and Divestiture Candidates. https://doi.org/10.2118/71425-ms
  3. Andrén, L., & Meddeb, J. (2021). Project portfolio management for AI projects. Developing a framework to manage the challenges with AI portfolios. https://hdl.handle.net/20.500.12380/302494
  4. Bartram, S. M., Branke, J., De Rossi, G., & Motahari, M. (2021). Machine Learning for Active Portfolio Management. Journal of Financial Data Science, 3(3), 9–30. https://doi.org/10.3905/JFDS.2021.1.071
  5. Beccalli, E., Elliot, V., & Virili, F. (2020). Artificial Intelligence and Ethics in Portfolio Management. Lecture Notes in Information Systems and Organisation, 38, 19–30. https://doi.org/10.1007/978-3-030-47355-6_2/COVER
  6. Boschke, H. (2018). Using Artificial Intelligence in Wealth Management. The WealthTech Book, 80–82. https://doi.org/10.1002/9781119444510.CH20
  7. Brasil, V. C., & Eggers, J. P. (2019). Product and Innovation Portfolio Management. https://doi.org/10.1093/acrefore/9780190224851.013.28
  8. Byrum, J. (2022). AI in Financial Portfolio Management: Practical Considerations and Use Cases. Springer Series in Supply Chain Management, 11, 249–270. https://doi.org/10.1007/978-3-030-75729-8_9/COVER
  9. Cameron, B. H. (2008). IS Project and Portfolio Management. https://doi.org/10.4018/978-1-59904-865-9.ch034
  10. Cheng, C. C., Wei, C. C., Chu, T. J., & Lin, H. H. (2022). AI Predicted Product Portfolio for Profit Maximization. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2083799
  11. Dash, G. H., & Kajiji, N. (2021). Behavioral Portfolio Management with Layered ESG Goals and Ai Estimation of Asset Returns. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3953440
  12. Elonen, S., & Artto, K. (2003). Problems in Managing Internal Development Projects in Multi-Project Environments. International Journal of Project Management. https://doi.org/10.1016/s0263-7863(02)00097-2
  13. Gao, X., Tu, S., & Xu, L. (2019). A* Tree Search for Portfolio Management. https://arxiv.org/abs/1901.01855v2
  14. Gautam, B., Gupta, S., Awasthi, S., & Gautam, P. S. (2019). Securities Analysis and Portfolio Management Using Artificial Neural Networks. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3332162
  15. Giudici, P., Pagnottoni, P., & Polinesi, G. (2020). Network Models to Enhance Automated Cryptocurrency Portfolio Management. Frontiers in Artificial Intelligence, 3, 510510. https://doi.org/10.3389/FRAI.2020.00022/BIBTEX
  16. Guan, M., & Liu, X. Y. (2021). Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach. ICAIF 2021 - 2nd ACM International Conference on AI in Finance. https://doi.org/10.1145/3490354.3494415
  17. Gupta, R., Mahajan, Y., Ahuja, P. M., & Ramteke, J. (2020). Portfolio Management Using Artificial Intelligence. 207–215. https://doi.org/10.1007/978-981-15-1059-5_24
  18. Holland, A., & Fathi, M. (2007). Quantitative and Qualitative Risk in IT Portfolio Management. https://doi.org/10.1109/icsmc.2007.4414057
  19. Hu, Y. J., & Lin, S. J. (2019). Deep Reinforcement Learning for Optimizing Finance Portfolio Management. Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 14–20. https://doi.org/10.1109/AICAI.2019.8701368
  20. Huang, G., Zhou, X., & Song, Q. (2020). Deep reinforcement learning for portfolio management. https://arxiv.org/abs/2012.13773v7
  21. Kaiser, M. G., El Arbi, F., & Ahlemann, F. (2015). Successful project portfolio management beyond project selection techniques: Understanding the role of structural alignment. International Journal of Project Management, 33(1), 126–139. https://doi.org/10.1016/J.IJPROMAN.2014.03.002
  22. Kulian, V. R., Korobova, M. V, & Yunkova, O. (2020). Optimal Stock Portfolio Diversification Under Market Constraints. System Research and Information Technologies. https://doi.org/10.20535/srit.2308-8893.2020.1.08
  23. Kulshrestha, N., Kamra, V., & Aggarwal, S. (2023). Leveraging technical analysis and artificial intelligence – optimisation of global portfolio management through world indices. International Journal of Public Sector Performance Management, 12(3), 445–461. https://doi.org/10.1504/IJPSPM.2023.133588
  24. Liang, Z., Chen, H., Zhu, J., Jiang, K., Li, Y., & Technology, L. (2018). Adversarial Deep Reinforcement Learning in Portfolio Management. https://arxiv.org/abs/1808.09940v3
  25. Lucarelli, G., & Borrotti, M. (2020). A deep Q-learning portfolio management framework for the cryptocurrency market. Neural Computing and Applications, 32(23), 17229–17244. https://doi.org/10.1007/S00521-020-05359-8/FIGURES/8
  26. Luo, Y., Kristal, B. S., Schweikert, C., & Hsu, D. F. (2017). Combining multiple algorithms for portfolio management using combinatorial fusion. Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017, 361–366. https://doi.org/10.1109/ICCI-CC.2017.8109774
  27. Marchinares, A. H., & Aguilar-Alonso, I. (2020). Project portfolio management studies based on machine learning and critical success factors. Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020, 369–374. https://doi.org/10.1109/PIC50277.2020.9350787
  28. Maree, C., & Omlin, C. W. (2022). Balancing Profit, Risk, and Sustainability for Portfolio Management. 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2022 - Proceedings. https://doi.org/10.1109/CIFER52523.2022.9776048
  29. Martínez, R. G. (2023). Portfolio Analysis With Sharpe Ratios Resampled With Bootstrapping. Economic Analysis Letters. https://doi.org/10.58567/eal02010004
  30. Rokade, A., Malhotra, A., & Wanchoo, A. (2017). Enhancing portfolio returns by identifying high growth companies in indian stock market using artificial intelligence. 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings, 262–266. https://doi.org/10.1109/RTEICT.2016.7807824
  31. Roman, S. (2004). Portfolio Management and the Capital Asset Pricing Model. 41–77. https://doi.org/10.1007/978-1-4419-9005-1_3
  32. Sendi, P. (2020). Dealing With Bad Risk in Cost-Effectiveness Analysis: The Cost-Effectiveness Risk-Aversion Curve. Pharmacoeconomics. https://doi.org/10.1007/s40273-020-00969-5
  33. Sharpe, W. F. (1966). Mutual Fund Performance. The Journal of Business, 39(1), 119–138.
  34. Soni, N., & Kumar, T. (2016). Optimum hedging tool of portfolio management using artificial intelligence and cloud computing in Indian stock market. International Journal of Computer Science and Information Security, 14(11), 1013.
  35. Sortino, F. A., & van der Meer, R. (1991). Downside risk: Capturing what's at stake in investment situations. Journal of Portfolio Management, 17(4), 27-31.
  36. Stefanus, A. C., & Robiyanto, R. (2020). Performance Evaluation of Exchange Traded Fund in the Indonesia Stock Exchange. International Journal of Social Science and Business. https://doi.org/10.23887/ijssb.v4i4.29422
  37. Teller, J., Kock, A., & Gemünden, H. G. (2014). Risk Management in Project Portfolios Is More Than Managing Project Risks: A Contingency Perspective on Risk Management. Project Management Journal. https://doi.org/10.1002/pmj.21431
  38. Ye, Y., Pei, H., Wang, B., Chen, P. Y., Zhu, Y., Xiao, J., & Li, B. (2020). Reinforcement-Learning Based Portfolio Management with Augmented Asset Movement Prediction States. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 1112–1119. https://doi.org/10.1609/AAAI.V34I01.5462
  39. Yu, X., Xie, S., & Xu, W. (2014). Optimal portfolio strategy under rolling economic maximum drawdown constraints. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/787943
  40. Zhang, X., & Chen, Y. (2017). An Artificial Intelligence Application in Portfolio Management. 775–793. https://doi.org/10.2991/ICTIM-17.2017.60